# Question 1: Tesla Stock Data with yfinance
import yfinance as yf

# Fetch Tesla data
tesla = yf.Ticker("TSLA")
tesla_data = tesla.history(period="max")

# Reset index and display first 5 rows
tesla_data.reset_index(inplace=True)
tesla_data.head()

	Date	Open	High	Low	Close	Volume	Dividends	Stock Splits
0	2010-06-29 00:00:00-04:00	1.266667	1.666667	1.169333	1.592667	281494500	0.0	0.0
1	2010-06-30 00:00:00-04:00	1.719333	2.028000	1.553333	1.588667	257806500	0.0	0.0
2	2010-07-01 00:00:00-04:00	1.666667	1.728000	1.351333	1.464000	123282000	0.0	0.0
3	2010-07-02 00:00:00-04:00	1.533333	1.540000	1.247333	1.280000	77097000	0.0	0.0
4	2010-07-06 00:00:00-04:00	1.333333	1.333333	1.055333	1.074000	103003500	0.0	0.0

# Question 2: Scrape Tesla Revenue Data
import pandas as pd
import requests
from bs4 import BeautifulSoup
from io import StringIO

# Fetch Tesla revenue data
url = "https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue"
headers = {"User-Agent": "Mozilla/5.0"}  # Mimic a browser request
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.text, "html.parser")

# Find the quarterly revenue table
tables = soup.find_all("table")
tesla_revenue = None

for table in tables:
    if "Quarterly Revenue" in str(table):
        tesla_revenue = pd.read_html(StringIO(str(table)))[0]
        tesla_revenue.columns = ["Date", "Revenue"]
        # Clean data
        tesla_revenue["Revenue"] = tesla_revenue["Revenue"].str.replace("$", "", regex=False).str.replace(",", "", regex=False)
        tesla_revenue = tesla_revenue[tesla_revenue["Revenue"] != ""]
        tesla_revenue["Revenue"] = pd.to_numeric(tesla_revenue["Revenue"])
        break

if tesla_revenue is not None:
    print("Last 5 rows of Tesla Revenue Data:")
    print(tesla_revenue.tail())
else:
    print("Error: Tesla revenue table not found!")

Last 5 rows of Tesla Revenue Data:
          Date  Revenue
58  2010-06-30     28.0
59  2010-03-31     21.0
60  2009-12-31      NaN
61  2009-09-30     46.0
62  2009-06-30     27.0

# Question 3: GameStop Stock Data with yfinance
import yfinance as yf

# Fetch GameStop stock data
gme = yf.Ticker("GME")
gme_data = gme.history(period="max")

# Reset index and display first 5 rows
gme_data.reset_index(inplace=True)
print(gme_data.head())

                       Date      Open      High       Low     Close    Volume  \
0 2002-02-13 00:00:00-05:00  1.620129  1.693350  1.603296  1.691667  76216000   
1 2002-02-14 00:00:00-05:00  1.712707  1.716074  1.670626  1.683250  11021600   
2 2002-02-15 00:00:00-05:00  1.683250  1.687458  1.658002  1.674834   8389600   
3 2002-02-19 00:00:00-05:00  1.666418  1.666418  1.578047  1.607504   7410400   
4 2002-02-20 00:00:00-05:00  1.615920  1.662209  1.603296  1.662209   6892800   

   Dividends  Stock Splits  
0        0.0           0.0  
1        0.0           0.0  
2        0.0           0.0  
3        0.0           0.0  
4        0.0           0.0  

# Question 4: Scrape GameStop Revenue Data
import pandas as pd
import requests
from bs4 import BeautifulSoup
from io import StringIO

# Fetch GameStop revenue data
url = "https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue"
headers = {"User-Agent": "Mozilla/5.0"}  # Mimic a browser request
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.text, "html.parser")

# Find the quarterly revenue table
tables = soup.find_all("table")
gme_revenue = None

for table in tables:
    if "Quarterly Revenue" in str(table):
        gme_revenue = pd.read_html(StringIO(str(table)))[0]
        gme_revenue.columns = ["Date", "Revenue"]
        # Clean data
        gme_revenue["Revenue"] = gme_revenue["Revenue"].str.replace("$", "", regex=False).str.replace(",", "", regex=False)
        gme_revenue = gme_revenue[gme_revenue["Revenue"] != ""]
        gme_revenue["Revenue"] = pd.to_numeric(gme_revenue["Revenue"])
        break

if gme_revenue is not None:
    print("Last 5 rows of GameStop Revenue Data:")
    print(gme_revenue.tail())
else:
    print("Error: GameStop revenue table not found!")

Last 5 rows of GameStop Revenue Data:
          Date  Revenue
60  2010-01-31     3524
61  2009-10-31     1835
62  2009-07-31     1739
63  2009-04-30     1981
64  2009-01-31     3492

import plotly.graph_objects as go

# Define the make_graph function (if not already defined)
def make_graph(stock_data, title):
    stock_data['Date'] = pd.to_datetime(stock_data['Date'])
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=stock_data['Date'], y=stock_data['Close'], name="Stock Price"))
    fig.update_layout(title=title, xaxis_title="Date", yaxis_title="Stock Price (USD)")
    fig.show()

# Generate the Tesla stock graph
print("\nGenerating Tesla Stock Graph...")
make_graph(tesla_data, "Tesla Stock Price History")

Generating Tesla Stock Graph...

def make_graph(stock_data, title):
    stock_data['Date'] = pd.to_datetime(stock_data['Date'])
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=stock_data['Date'], y=stock_data['Close'], name="Stock Price"))
    fig.update_layout(title=title, xaxis_title="Date", yaxis_title="Stock Price (USD)")
    fig.show()

# Generate the GameStop stock graph
print("\nGenerating GameStop Stock Graph...")
make_graph(gme_data, "GameStop Stock Price History")

Generating GameStop Stock Graph...